mou3az/IT-General_Question-Generation

    from peft import PeftModel, PeftConfig
    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
    HUGGING_FACE_USER_NAME = "mou3az"
    model_name = "IT-General_Question-Generation "
    peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}"
    config = PeftConfig.from_pretrained(peft_model_id)
    model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto')
    QG_tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
    QG_model = PeftModel.from_pretrained(model, peft_model_id)

    def get_question(context, answer):
        device = next(QG_model.parameters()).device
        input_text = f"Given the context '{context}' and the answer '{answer}', what question can be asked?"
        encoding = QG_tokenizer.encode_plus(input_text, padding=True, return_tensors="pt").to(device)
    
        output_tokens = QG_model.generate(**encoding, early_stopping=True, num_beams=5, num_return_sequences=1, no_repeat_ngram_size=2, max_length=100)
        out = QG_tokenizer.decode(output_tokens[0], skip_special_tokens=True).replace("question:", "").strip()
    
        return out